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Team-building with answer set programming in the Gioia-Tauro seaport

Published online by Cambridge University Press:  02 June 2011

F. RICCA ,
G. GRASSO ,
M. ALVIANO ,
M. MANNA ,
V. LIO ,
S. IIRITANO  and
N. LEONE
F. RICCA
Affiliation:
Dipartimento di Matematica, Università della Calabria, 87030 Rende, Italy (e-mail: ricca@mat.unical.it)
G. GRASSO
Affiliation:
Dipartimento di Matematica, Università della Calabria, 87030 Rende, Italy; and Computing Laboratory, University of Oxford, Oxford, UK (e-mail: grasso@mat.unical.it)
M. ALVIANO
Affiliation:
Dipartimento di Matematica, Università della Calabria, 87030 Rende, Italy (e-mail: alviano@mat.unical.it, manna@mat.unical.it)
M. MANNA
Affiliation:
Dipartimento di Matematica, Università della Calabria, 87030 Rende, Italy (e-mail: alviano@mat.unical.it, manna@mat.unical.it)
V. LIO
Affiliation:
Exeura s.r.l., Via Pedro Alvares Cabrai – C.da Lecco 87036 Rende (CS), Italy (e-mail: vincenzino.lio@exeura.it, salvatore.iiritano@exeura.it)
S. IIRITANO
Affiliation:
Exeura s.r.l., Via Pedro Alvares Cabrai – C.da Lecco 87036 Rende (CS), Italy (e-mail: vincenzino.lio@exeura.it, salvatore.iiritano@exeura.it)
N. LEONE
Affiliation:
Dipartimento di Matematica, Università della Calabria, 87030 Rende, Italy (e-mail: leone@mat.unical.it)
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Abstract

The seaport of Gioia Tauro is the largest transshipment terminal of the Mediterranean coast. A crucial management task for the companies operating in the seaport is team-building: the problem of properly allocating the available personnel for serving the incoming ships. Teams have to be carefully arranged in order to meet several constraints, such as allocation of employees with appropriate skills, fair distribution of the working load, and turnover of the heavy/dangerous roles. This makes team-building a hard and expensive task requiring several hours of manual preparation per day.

In this paper we present a system based on Answer Set Programming for the automatic generation of the teams of employees in the seaport of Gioia Tauro. The system is currently exploited in the Gioia Tauro seaport by ICO BLG, a company specialized in automobile logistics.

Keywords

answer set programmingdeclarative problem solvingknowledge managementworkforce managementartificial intelligence
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 12 , Issue 3 , May 2012 , pp. 361 - 381
Copyright
Copyright © Cambridge University Press 2011

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References

Aickelin, U. and Dowsland, K. A. 2000. Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem. Journal of Scheduling 3, 3, 139–153.3.0.CO;2-2>CrossRefGoogle Scholar
Al-Yakoob, S. and Sherali, H. 2007. Mixed-integer programming models for an employee scheduling problem with multiple shifts and work locations. Annals of Operations Research 155, 1, 119–142.CrossRefGoogle Scholar
Alba, E. and Chicano, J. F. 2007. Software project management with GAs. Information Sciences 177, 11, 2380–2401.Google Scholar
Alfares, H. K. 2002. Optimum workforce scheduling under the (14, 21) days-off timetable. Journal of Applied Mathematics & Decision Sciences 6, 3, 191–199.CrossRefGoogle Scholar
Balduccini, M., Gelfond, M., Watson, R. and Nogueira, M. 2001. The USA-advisor: A case study in answer set planning. In Proceedings of LPNMR '01. LNCS, vol. 2173. Springer, Berlin, 439–442.Google Scholar
Baral, C. 2003. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge, UK.Google Scholar
Baral, C. and Gelfond, M. 2000. Reasoning agents in dynamic domains. In Logic-Based Artificial Intelligence. Kluwer Academic Publishers, Norwell, MA, 257–279.CrossRefGoogle Scholar
Baral, C. and Uyan, C. 2001. Declarative specification and solution of combinatorial auctions lsing logic programming. In Logic Programming and Nonmotonic Reasoning. LNCS, vol. 2173. Springer, Berlin, 186–199.Google Scholar
Bardadym, V. 1996. Computer-aided school and university timetabling: The new wave. In Practice and Theory of Automated Timetabling. LNCS, vol. 1153. Springer, Berlin, 22–45.CrossRefGoogle Scholar
Bechtold, S. E., Brusco, M. J. and Showalter, M. J. 1991. A comparative evaluation of labor tour scheduling methods. Decision Sciences 22, 4, 683–699.Google Scholar
Billionnet, A. 1999. Integer programming to schedule a hierarchical workforce with variable demands. European Journal of Operational Research 114, 1, 105–114.CrossRefGoogle Scholar
Burke, E. K. and Soubeiga, E. 2003. A real-world workforce scheduling problem in the hospitality industry: Theoretical models and algorithmic methods [online]. URL: http://webhost.ua.ac.be/eume/workshops/reallife/burke.pdfGoogle Scholar
Cerulli, R., Gaudioso, M. and Mautone, R. 1992. A class of manpower scheduling problems. Mathematical Methods of Operations Research 36, 1, 93–105.CrossRefGoogle Scholar
Chiu, D. K. W., Cheung, S. C. and Leung, H.-F. 2005. A multi-agent infrastructure for mobile workforce management in a service-oriented enterprise. In Proceedings of HICSS '05. IEEE Computer Society, 85c.Google Scholar
Dechter, R. 2004. Constraint Processing. Morgan Kaufmann, Massachusetts.Google Scholar
Eiter, T., Faber, W., Leone, N. and Pfeifer, G. 2000. Declarative problem-solving using the DLV system. In Logic-Based Artificial Intelligence. Kluwer Academic Publishers, Norwell, MA, 79–103.Google Scholar
Eitzen, G., Panton, D. and Mills, G. 2004. Multi-skilled workforce optimisation. Annals of Operations Research 127, 1, 359–372.Google Scholar
Ernst, A. T., Jiang, H., Krishnamoorthy, M. and Sier, D. 2004. Staff scheduling and rostering: A review of applications, methods and models. European Journal of Operational Research 153, 1, 3–27.CrossRefGoogle Scholar
Faber, W., Leone, N. and Pfeifer, G. 2004. Recursive aggregates in disjunctive logic programs: Semantics and complexity. In Logics in Artificial Intelligence. LNCS, vol. 3229. Springer, Berlin, 200–212.CrossRefGoogle Scholar
Franconi, E., Palma, A., Leone, N., Perri, S. and Scarcello, F. 2001. Census data repair: A challenging application of disjunctive logic programming. In International Conference on Logic for Programming, Artificial Intelligence, and Reasoning. LNCS, vol. 2250. Springer, Berlin, 561–578.Google Scholar
Friedrich, G. and Ivanchenko, V. 2008. Diagnosis From First Principles for Workflow Executions. Technical Report 2, Alpen-Adria-Universitt, Klagenfurt.Google Scholar
Gebser, M., Liu, L., Namasivayam, G., Neumann, A., Schaub, T. and Truszczyńskii, M. 2007. The first answer set programming system competition. In International Conference on Logic Programming and Nonmonotonic Reasoning. LNCS, vol. 4483. Springer, Berlin, 3–17.Google Scholar
Gelfond, M. and Leone, N. 2002. Logic programming and knowledge representation-the A-prolog perspective. Artificial Intelligence 138, 1–2, 3–38.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385.CrossRefGoogle Scholar
Grasso, G., Iiritano, S., Leone, N. and Ricca, F. 2009. Some DLV applications for knowledge management. In International Conference on Logic Programming and Nonmonotonic Reasoning. LNCS, vol. 5753. Springer, Berlin, 591–597.Google Scholar
Gresh, D. L., Connors, D. P., Fasano, J. P. and Wittrock, R. J. 2007. Applying supply chain optimization techniques to workforce planning problems. IBM Journal of Research Development 51, 3, 251–261.Google Scholar
Hultberg, T. H. and Cardoso, D. M. 1997. The teacher assignment problem: A special case of the fixed charge transportation problem. European Journal of Operational Research 101, 3, 463–473.Google Scholar
Lau, H. C. 1996. On the complexity of manpower shift scheduling. Computers & Operations Research 23, 1, 93–102.Google Scholar
Lee, J. and Meng, Y. 2009. On reductive semantics of aggregates in answer set programming. In International Conference on Logic Programming and Nonmonotonic Reasoning. LNCS, vol. 5753. Springer, Berlin, 182–195.CrossRefGoogle Scholar
Leone, N., Greco, G., Ianni, G., Lio, V., Terracina, G., Eiter, T., Faber, W., Fink, M., Gottlob, G., Rosati, R., Lembo, D., Lenzerini, M., Ruzzi, M., Kalka, E., Nowicki, B. and Staniszkis, W. 2005. The INFOMIX system for advanced integration of incomplete and inconsistent data. In Proceedings of SIGMOD '05, Baltimore, Maryland. ACM, New York, NY, 915–917.Google Scholar
Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S. and Scarcello, F. 2006. The DLV system for knowledge representation and reasoning. ACM Transactionsof Computers Logic 7, 3, 499–562.CrossRefGoogle Scholar
Lesaint, D., Voudouris, C., Azarmi, N., Alletson, I. and Laithwaite, B. 2003. Field workforce scheduling. BT Technology Journal 21, 4, 23–26.CrossRefGoogle Scholar
Naveh, Y., Richter, Y., Altshuler, Y., Gresh, D. L. and Connors, D. P. 2007. Workforce optimization: Identification and assignment of professional workers using constraint programming. IBM Journal of Research and Development 51, 3–4, 263–279.Google Scholar
Nogueira, M., Balduccini, M., Gelfond, M., Watson, R. and Barry, M. 2001. An A-Prolog decision support system for the space shuttle. In International Conference on Practical Aspects of Declarative Languages. LNCS, vol. 1990. Springer, Berlin, 169–183.Google Scholar
Ricca, F., Gallucci, L., Schindlauer, R., Dell'Armi, T., Grasso, G. and Leone, N. 2009. OntoDLV: An ASP-based system for enterprise ontologies. Journal of Logic Computation 19, 4, 643–670.Google Scholar
Rossi, F. 2000. Constraint (logic) programming: A survey on research and applications. In New Trends in Constraints Joint ERCIM/Compulog Net Workshop, 25-27 Oct. 1999, Paphos, Cyprus. LNCS, vol. 1865. Springer, Berlin, 40–74.Google Scholar
Sun, M., Aronson, J. E., McKeown, P. G. and Drinka, D. 1998. A tabu search heuristic procedure for the fixed charge transportation problem. European Journal of Operational Research 106, 2–3, 441–456.CrossRefGoogle Scholar
Tien, J. M. and Kamiyama, A. 1982. On manpower scheduling algorithms. SIAM Review 24, 3, 275–287.Google Scholar
Vacca, I., Bierlaire, M. and Salani, M. 2007. Optimization at container terminals: Status, trends and perspectives. In Proceedings of the Swiss Transport Research Conference, Ascona, Switzerland, September 14, 2007.Google Scholar
Wren, A. and Wren, D. O. 1995. A genetic algorithm for public transport driver scheduling. Computers & Operations Research 22, 1, 101–110.Google Scholar
Yang, R. 1996. Solving a workforce management problem with constraint programming. In Proceedings of PACT '96. Practical Application Company, Blackpool, Lancashire, UK, 373–387.Google Scholar